[ENH] Add TargetTransform and DistrPredictiveCalibration for probabilistic regression

[arnavk23]

Reference Issues/PRs

Towards #7

What does this implement/fix? Explain your changes.

This PR adds two probabilistic regression pipeline wrappers:

• TargetTransform fits a regressor on a transformed target and inverse-transforms predictions back to the original target scale.
• DistrPredictiveCalibration wraps a probabilistic regressor and applies a calibrator to its predicted distributions.

Does your contribution introduce a new dependency? If yes, which one?

No.

What should a reviewer concentrate their feedback on?

• Correctness of the inverse transformation logic in TargetTransform, specially for distribution location and scale.
• Whether DistrPredictiveCalibration applies calibration at the right points in the prediction flow.
• Whether the test-only calibrators are acceptable as implemented, or should be moved further out.

Did you add any tests for the change?

Added tests that verify:

• TargetTransform with StandardScaler correctly inverse-transforms predictive distributions.
• DistrPredictiveCalibration actually changes the predicted distribution. Calibration also affects quantile predictions.

Any other comments?

PR checklist

For all contributions

• I've added myself to the list of contributors with any new badges I've earned :-)
• The PR title starts with either [ENH], [MNT], [DOC], or [BUG]. [BUG] - bugfix, [MNT] - CI, test framework, [ENH] - adding or improving code, [DOC] - writing or improving documentation or docstrings.

For new estimators

• I've added the estimator to the API reference - in docs/source/api_reference/taskname.rst, follow the pattern.
• I've added one or more illustrative usage examples to the docstring, in a pydocstyle compliant Examples section.
• If the estimator relies on a soft dependency, I've set the python_dependencies tag and ensured
  dependency isolation, see the estimator dependencies guide.